| 1 | """Integration test: tool_use_fidelity end-to-end on a real tiny adapter. |
| 2 | |
| 3 | Builds a tiny random LoRA on SmolLM2-135M-Instruct and runs the probe |
| 4 | against a single tool-use case. The intent isn't to assert that the |
| 5 | 135M base produces useful tool calls — it almost certainly won't — |
| 6 | but to exercise the full code path on a real backend so a regression |
| 7 | in the JSON-extraction / schema-check / hallucination plumbing |
| 8 | surfaces in slow CI rather than only at user-fix time. |
| 9 | |
| 10 | Marked ``slow + online``. |
| 11 | """ |
| 12 | |
| 13 | from __future__ import annotations |
| 14 | |
| 15 | from pathlib import Path |
| 16 | |
| 17 | import pytest |
| 18 | |
| 19 | pytestmark = [pytest.mark.slow, pytest.mark.online] |
| 20 | |
| 21 | |
| 22 | def _build_random_lora_adapter(base_dir: Path, out_dir: Path) -> None: |
| 23 | """A trivially-random LoRA over q_proj/v_proj — same shape used by |
| 24 | the other slow-lane backend tests.""" |
| 25 | import torch |
| 26 | from peft import LoraConfig, get_peft_model |
| 27 | from transformers import AutoModelForCausalLM, AutoTokenizer |
| 28 | |
| 29 | torch.manual_seed(0) |
| 30 | tokenizer = AutoTokenizer.from_pretrained(str(base_dir)) |
| 31 | if tokenizer.pad_token_id is None: |
| 32 | tokenizer.pad_token = tokenizer.eos_token |
| 33 | base = AutoModelForCausalLM.from_pretrained(str(base_dir), torch_dtype=torch.float32) |
| 34 | cfg = LoraConfig( |
| 35 | r=8, |
| 36 | lora_alpha=16, |
| 37 | target_modules=["q_proj", "v_proj"], |
| 38 | lora_dropout=0.0, |
| 39 | bias="none", |
| 40 | task_type="CAUSAL_LM", |
| 41 | ) |
| 42 | peft_model = get_peft_model(base, cfg) |
| 43 | with torch.no_grad(): |
| 44 | for name, param in peft_model.named_parameters(): |
| 45 | if "lora_B" in name: |
| 46 | # Tiny perturbation — enough that base.generate ≠ ft.generate |
| 47 | # but small enough to keep generations finite + sensible. |
| 48 | param.copy_(torch.randn_like(param) * 0.01) |
| 49 | peft_model.save_pretrained(str(out_dir)) |
| 50 | tokenizer.save_pretrained(str(out_dir)) |
| 51 | |
| 52 | |
| 53 | @pytest.fixture(scope="module") |
| 54 | def random_adapter(tiny_model_dir: Path, tmp_path_factory: pytest.TempPathFactory) -> Path: |
| 55 | out = tmp_path_factory.mktemp("tool-use-fidelity-adapter") |
| 56 | _build_random_lora_adapter(tiny_model_dir, out) |
| 57 | return out |
| 58 | |
| 59 | |
| 60 | def test_probe_runs_end_to_end_on_real_adapter(tiny_model_dir: Path, random_adapter: Path) -> None: |
| 61 | """Smoke: HF backend + real adapter + probe execution returns a finalized |
| 62 | result with all evidence keys populated. SmolLM2-135M can't reliably |
| 63 | emit OpenAI-shape calls, so the probe will likely FAIL the validity |
| 64 | floor — what we assert here is that it produced a structured verdict |
| 65 | + finite metrics, not a particular pass/fail outcome.""" |
| 66 | from dlm_sway.backends.hf import HuggingFaceDifferentialBackend |
| 67 | from dlm_sway.core.model import ModelSpec |
| 68 | from dlm_sway.core.result import Verdict |
| 69 | from dlm_sway.probes.base import RunContext, build_probe |
| 70 | |
| 71 | backend = HuggingFaceDifferentialBackend( |
| 72 | base_spec=ModelSpec(base=str(tiny_model_dir), kind="hf", dtype="fp32", device="cpu"), |
| 73 | adapter_path=random_adapter, |
| 74 | ) |
| 75 | try: |
| 76 | probe, spec = build_probe( |
| 77 | { |
| 78 | "name": "tuf_smoke", |
| 79 | "kind": "tool_use_fidelity", |
| 80 | "cases": [ |
| 81 | { |
| 82 | "prompt": ( |
| 83 | "You are a tool-using assistant. The user asks: " |
| 84 | "search the web for cats. Reply with ONLY a JSON " |
| 85 | 'object of the form {"name": ..., "arguments": {...}}.' |
| 86 | ), |
| 87 | "tool_spec": { |
| 88 | "name": "search_web", |
| 89 | "parameters": { |
| 90 | "type": "object", |
| 91 | "properties": { |
| 92 | "query": {"type": "string"}, |
| 93 | "max_results": {"type": "integer"}, |
| 94 | }, |
| 95 | "required": ["query"], |
| 96 | }, |
| 97 | }, |
| 98 | "gold_tool_name": "search_web", |
| 99 | "max_new_tokens": 64, |
| 100 | } |
| 101 | ], |
| 102 | "allowed_tools": ["search_web"], |
| 103 | } |
| 104 | ) |
| 105 | ctx = RunContext(backend=backend, seed=0) |
| 106 | result = probe.run(spec, ctx) |
| 107 | finally: |
| 108 | backend.close() |
| 109 | |
| 110 | # End-to-end shape: a verdict came back, evidence carries every |
| 111 | # documented key, and the rates are in [0, 1]. |
| 112 | assert result.verdict in {Verdict.PASS, Verdict.FAIL, Verdict.WARN}, result.message |
| 113 | assert result.evidence["num_cases"] == 1 |
| 114 | for key in ( |
| 115 | "json_valid_rate_base", |
| 116 | "json_valid_rate_ft", |
| 117 | "validity_delta", |
| 118 | "mean_arg_disagreement", |
| 119 | "hallucination_rate", |
| 120 | ): |
| 121 | assert key in result.evidence, f"missing evidence key {key}" |
| 122 | assert 0.0 <= result.evidence["json_valid_rate_ft"] <= 1.0 |
| 123 | assert 0.0 <= result.evidence["json_valid_rate_base"] <= 1.0 |
| 124 | assert -1.0 <= result.evidence["validity_delta"] <= 1.0 |
| 125 | assert 0.0 <= result.evidence["hallucination_rate"] <= 1.0 |